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Record W4323353346 · doi:10.1093/jcag/gwac036.064

A64 THE UPTAKE AND IMPACT OF AN ELECTRONIC CIRRHOSIS ADMISSION ORDER SET: AN EARLY EXPERIENCE AT A SINGLE CENTRE

2023· article· en· W4323353346 on OpenAlexaff
KINNER M PATEL, M Eissa, V V Nguyen, J G Abraldes, Jeremy Theal, Erik Johnson, A Hyde, P Tandon

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineCirrhosisLogistic regressionPopulationGuidelineEmergency medicineComorbidityHealth careIntensive care medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Cirrhosis is a chronic disease that confers high morbidity and mortality. It is a leading cause for hospital admissions and leads to significant healthcare resource utilization. Several guidelines outline recommendations to provide best practice to hospitalized patients with cirrhosis. Despite studies supporting a reduction in mortality when guideline based care is followed, this is achieved in less than 50% of hospitalized patients with cirrhosis1. Standardized electronic order sets can be a potential tool to improving clinical outcomes and bridging this gap in care. Purpose Since March 2021, an electronic cirrhosis admission order set has been available for at our hospital site. Using administrative data, we aimed to describe our early experience with: a) order set uptake by various services, b) characteristics of the population in which the order set was used versus not used, and explore c) the impact of order set use on in-hospital mortality. Method In this single centre cohort study, patients with cirrhosis were identified based an administrative data algorithm containing codes for cirrhosis and complications. This data was used to retrieve parameters such as patient age, sex, primary admitting service, resource intensity weight (RIW), Charlson comorbidity index (CCI) and in-hospital mortality. The chi-squared test and independent samples t-test were used to compare characteristics of patients in whom the order set was used versus not used. Multivariable logistic regression was used to determine the impact of order set use on in-hospital mortality. P value significance was established at <0.05. Result(s) A total of 825 patients were included in the analysis. The overall mean age (standard deviation) of patients was 58.5 (14.2) years with 57.5% being male. Average length of stay was 11.3 days with a mean CCI of 3.2 (2.3) and RIW of 3.3 (7.2). The primary admitting service was Gastroenterology in 36.1%, Internal Medicine in 35.6% and other services in 28.3% of cases. Of those admitted, the order set was used in 27.2% of cases. The overall in-hospital mortality of patients was 14.2%. Mean age, sex and CCI were not significantly different in patients admitted with the order set versus without. In patients admitted with the order set compared to without, RIW was significantly lower (2.06 (2.62) versus 3.80 (8.2), p<0.001), as was length of stay (9.5 (11.8) days compared to 12.0 (18.6) days, p =0.03) and in-hospital mortality (8.5% versus 16.3%, p =0.003). On multivariable regression analysis (Table 1), after adjustment for age, RIW and CCI, use of the order set was associated with lower in-hospital mortality (odds ratio 0.53 (95% CI 0.3 to 0.9), p=0.02). Image Conclusion(s) Uptake of the electronic cirrhosis admission order set was modest at only 27% of eligible admissions. Although it appears to be associated with lower in-hospital mortality, a chart review is in process to assess if this association still holds after accounting for the impact of additional confounders. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.392
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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